A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design

By D. Gorissen, Ivo Couckuyt, Piet Demeester, Tom D'haene, K. Crombecq. 2010

550 citations recorded by the source corpus.

Subfields: Building and Construction, Management Science and Operations Research

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In Building and Construction

In Management Science and Operations Research

Citations in the corpus

Recent advances and applications of surrogate models for finite element method computations: a review (2022, methodology): “MATSuMoTo can also compute several function evaluations in parallel by utilizing the Parallel Computing Toolbox available in MATLAB.”
A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design (2017, methodology): “This approach has been involved in a MATLAB toolbox SUrrogate MOdeling (SUMO) (Gorissen et al. 2010) for adaptively refining metamodels.”
Managing computational complexity using surrogate models: a critical review (2020, background): “In active learning, there is a function for minimizing the number of samples chosen during the iterations and maximizing the knowledge attained with each step (Gorissen et al. 2010).”
Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions (2014, background): “…of the complex model inputs into the outputs, namely objective functions and/or constraints (Bieker et al., 2007; Blanning, 1975; Broad et al., 2005, 2010; Gorissen et al., 2010; Keating et al., 2010; Kleijnen, 2009; Laloy et al., 2013; Queipo et al., 2005; Yan and Minsker, 2006, 2011).”
Incremental modeling of a new high-order polynomial surrogate model (2016, unknown): “sampling [41], and incremental sampling [42]) has been developed.”
Data-driven operator inference for nonintrusive projection-based model reduction (2016, methodology): “In contrast to projection-based reduced models, data-fit surrogate models are constructed using interpolation and regression techniques to directly learn the map from inputs to outputs of the full model [19, 20, 21, 22].”
A Python surrogate modeling framework with derivatives (2019, methodology): “Various packages that build surrogate models have been developed using different programming languages, such as Scikit-learn in Python [1], SUMO in MATLAB [2], and GPML in MATLAB and Octave [3].”
Learning surrogate models for simulation‐based optimization (2014, methodology): “Similar existing modeling packages, such as SUorrogate MOdeling lab toolbox (SUMO) [18], fail to generate surrogate models with sufficiently low complexity.”
A Gaussian Process Surrogate Model Assisted Evolutionary Algorithm for Medium Scale Expensive Optimization Problems (2014, methodology): “Our pilot experiments have shown that when using 150 training data, it can take 240–400 s to build a single GP model for an objective function with 50 variables when the Blind DACE toolbox [20] is used on a Xeon 2.”
Surrogate Benchmarks for Hyperparameter Optimization (2014, methodology): “Similarly, the SUrrogate MOdeling (SUMO) Matlab toolkit[10] provides an environment for building regression models to describe the outputs of expensive computer simulations based on active learning.”